US 7212150 B2 Abstract A method is provided for detecting a target signal of a specific known form in the presence of clutter. The method includes dividing a set of initial training data, derived from returns from a burst of identical pulses, into a set of censored data and a set of uncensored data. A covariance matrix estimate, based on the uncensored data, is used to compute adaptive coherence estimate values, and an average adaptive coherence estimate threshold level is computed for each Doppler band to obtain a corresponding threshold. The censored data and the covariance matrix estimate are used to compute adaptive coherence estimate values for the uncensored data for each Doppler band, and these values are compared with the respective thresholds to determine the presence or absence of the target signal.
Claims(22) 1. A method for selecting a threshold for an adaptive coherence estimate detector for an airborne radar application and detecting a target signal using the threshold wherein a specific form of a target signal to be detected is known, said method comprising the steps of:
determining a Doppler frequency for a clutter ridge of a clutter return from ground of a radar beam transmitted from an airborne radar antenna;
determining the proximity of a Doppler band of interest to the Doppler frequency of clutter ridge; and
setting the threshold of the adaptive coherence estimate detector based on the proximity of the Doppler band of interest to the clutter ridge.
2. A method according to
wherein input data in the respective channels are sampled to form range cell samples for each pulse,
wherein snapshots are formed by stacking, in succession, N-length data vectors associated with each of the channels for each of the M pulses,
wherein signal presence is sought in one range cell at a time,
wherein the snapshots are censored so as to divide a set of K initial training data into (i) a set of K
_{c }censored training data snapshots that may potentially contain a target, and (ii) a set of K_{u }uncensored training data snapshots,wherein a covariance estimate is computed based on the uncensored snapshots,
wherein the covariance estimate is used in computing an adaptive coherence estimate values for the K
_{c }censored snapshots for each of the M Doppler frequency bands and in computing a quiescent adaptive coherence estimate threshold level for the K_{u }uncensored snapshots for each of the M Doppler frequency bands,wherein threshold levels so computed are averaged over the uncensored range cells to yield a M-length threshold vector wherein each vector element corresponds to the quiescent estimate level for a particular Doppler; and
wherein the adaptive coherence estimate values for the K
_{c }censored snapshots are compared with a corresponding quiescent estimate level to detect the presence or absence of a target signal.3. A method according to
_{c}, is used to compute the adaptive coherence estimate values, denoted ACE_{CTD}(m,k_{c}), for the set of k_{c}=1, 2 . . . K_{c }censored training data snapshots, denoted z_{CTD,k} _{ c }, for each of the m=1, 2, . . . , M Doppler frequency bands, using a steering vector s_{m }in accordance with the equation:wherein H denotes the Hermitian matrix and s
_{m}, {tilde over (R)}_{c}, and z_{CTD,k} _{ c }are defined as above.4. A method according to
_{c}, is used to compute the adaptive coherence estimate levels, denoted ACE_{UTD}(m,k_{u}), for the set of k_{u}=1, 2 . . . K_{u }uncensored training data snapshots, denoted z_{UTD,k} _{ u }, for each of the m=1, 2, . . . , M Doppler frequency bands, using a steering vector s_{m }in accordance with the equation:wherein H denotes the Hermitian matrix, and z
_{UTD,k} _{ u }, s_{m }and {tilde over (R)}_{c }are defined as above.5. A method according to
6. A method according to
7. A method according to
8. A method according to
_{m}, for the m^{th }Doppler band is scaled as
τ _{m}=max(βγ_{m},τ_{min})wherein β is a scale factor used to set a desired probability of false alarm which is constant over m=1, 2, . . . , M, γ
_{m }is the value of the threshold for the m^{th }Doppler, and τ_{min }is a minimum desired threshold level.9. A method for detecting a signal of a specific known form in the presence of clutter, said method comprising:
(i) dividing a set of initial training data derived from returns from a burst of identical pulses, into a set of censored data and a set of uncensored data;
(ii) using the uncensored data to compute a covariance matrix estimate;
(iii) using the covariance matrix estimate to compute adaptive coherence estimate values;
(iv) computing an average adaptive coherence estimate threshold for each Doppler band so as to obtain a threshold;
(v) using the censored data of step (i) and the covariance matrix estimate of step (ii) to compute adaptive coherence estimate values for the uncensored data for each Doppler band; and
(vi) comparing the adaptive coherence estimate values computed in step (v) with the respective thresholds computed in step (iv) to determine the presence or absence of the signal of a specific known form.
10. A method according to
_{c}, is used to compute the adaptive coherence estimate values, denoted ACE_{CTD}(m,k_{c}), for the set of k_{c}=1, 2 . . . K_{c }censored training data snapshots, denoted z_{CTD,k} _{ c }, for each of the m=1, 2, . . . , M Doppler frequency bands, using a steering vector s_{m }in accordance with the equation:wherein H denotes the Hermitian matrix and s
_{m}, {tilde over (R)}_{c}, and z_{CTD,k} _{ c }are defined as above.11. A method according to
_{c}, is used to compute the adaptive coherence estimate levels, denoted ACE_{UTD}(m,k_{u}), for the set of k_{u}=1, 2 . . . K_{u }uncensored training data snapshots, denoted z_{UTD,k} _{ u }, for each of the m=1, 2, . . . , M Doppler frequency bands, using a steering vector s_{m }in accordance with the equation:wherein H denotes the Hermitian matrix, and z
_{UTD,k} _{ u }, s_{m }and {tilde over (R)}_{c }are defined as above.12. A method according to
13. A method according to
14. A method according to
15. A method according to
_{m}, for the m^{th }Doppler band is scaled as
τ _{m}=max(βγ_{m},τ_{min})wherein β is a scale factor used to set a desired probability of false alarm which is constant over m=1, 2, . . . , M, γ
_{m }is the value of the threshold for the m^{th }Doppler, and τ_{min }is a minimum desired threshold level.16. A method for selecting a threshold for an adaptive coherence estimate detector and detecting a target signal using the threshold wherein a specific form of a target signal to be detected is known, said method comprising the steps of:
receiving returns from a burst of M identical pulses transmitted over N radio frequency channels,
sampling input data in the respective channels to form range cell samples for each pulse,
forming snapshots by stacking, in succession, N-length data vectors associated with each of the channels for each of the M pulses,
censoring the snapshots so as to divide a set of K initial training data into (i) a set of K
_{c }censored training data snapshots that may potentially contain a target, and (ii) a set of K_{u }uncensored training data snapshots,computing a covariance estimate based on the uncensored snapshots,
using the covariance estimate in computing an adaptive coherence estimate values for the K
_{c }censored snapshots for each of the M Doppler frequency bands and in computing a quiescent adaptive coherence estimate threshold level for the K_{u }uncensored snapshots for each of the M Doppler frequency bands,averaging the threshold levels so computed over the range cells corresponding to the uncensored snapshots to yield a M-length threshold vector wherein each vector element corresponds to the quiescent adaptive coherence estimate level for a particular Doppler; and
comparing the adaptive coherence estimate values for the K
_{c }censored snapshots with corresponding adaptive coherence estimate quiescent levels to detect the presence or absence of the target signal.17. A method according to
_{c}, is used to compute the adaptive coherence estimate values, denoted ACE_{CTD}(m,k_{c}), for the set of k_{c}=1, 2 . . . K_{c }censored training data snapshots, denoted Z_{CTD,k} _{ c }, for each of the m=1, 2, . . . , M Doppler frequency bands, using a steering vector s_{m }in accordance with the equation:wherein H denotes the Hermitian matrix and s
_{m}, {tilde over (R)}_{c}, and z_{CTD,k} _{ c }are defined as above.18. A method according to
_{c }is used to compute the adaptive coherence estimate levels, denoted ACE_{UTD}(m,k_{u}), for the set of k_{u}=1, 2 . . . K_{u }uncensored training data snapshots, denoted z_{UTD,k} _{ u }, for each of the m=1, 2, . . . , M Doppler frequency bands, using a steering vector s_{m }in accordance with the equation:wherein H denotes the Hermitian matrix, and Z
_{UTD,k} _{ u }, s_{m }and {tilde over (R)}_{c }are defined as above.19. A method according to
20. A method according to
21. A method according to
22. A method according to
_{m}, for the m^{th }Doppler band is scaled as
τ _{m}=max(βγ_{m},τ_{min})wherein β is a scale factor used to set a desired probability of false alarm which is constant over m=1, 2, . . . , M, γ
_{m }is the value of the threshold for the m^{th }Doppler, and τ_{min }is a minimum desired threshold level.Description The present invention generally relates to adaptive coherence estimate detectors, particularly for, but not limited to, airborne radar applications. In applications such as radar, sonar, data communications, time series analysis, and array processing, an object is to determine whether a specific signal is present in a series of N measured data samples (which can be represented as Z=[z(0), z(1), . . . , z(N−1)] An Adaptive Coherence Estimate (ACE) detector, which is also known as an Adaptive Cosine Detector, is one such ASD in which the specific form of the desired signal is known (as opposed to detectors that test for the presence of any signal that lies within the signal subspace), but the power level of the noise and interference is unknown (see, e.g., L. L. Scharf and L. T. McWhorter, “Adaptive matched subspace detectors and adaptive coherence estimators,” Generally speaking, one aspect of the present invention concerns a method for selecting the threshold for the ACE detector, and, more particularly, in preferred embodiments, selecting the threshold for airborne radar applications in which a form of censored Space-Time Adaptive Processing (STAP) is employed. An important feature of preferred embodiments of the invention is that the coherent nature of the ACE test statistic thereby enables an appropriate threshold to be set for each individual Doppler frequency band, hence resulting in substantially improved target signal detection performance as compared with a conventional “uniform threshold across” Doppler system. In accordance with one aspect of the invention, there is provided a method for selecting a threshold for an adaptive coherence estimate detector for an airborne radar application and detecting a target signal using the threshold wherein a specific form of a target signal to be detected is known, said method comprising the steps of: determining a Doppler frequency for a clutter ridge of a clutter return from ground of a radar beam transmitted from an airborne radar antenna; determining the proximity of a Doppler band of interest to the Doppler frequency of clutter ridge; and setting the threshold of the adaptive coherence estimate detector based on the proximity of the Doppler band of interest to the clutter ridge. Preferably, target detection is based on returns from a burst of M identical pulses transmitted over N radio frequency channels, input data in the respective channels are sampled to form range cell samples for each pulse, snapshots are formed by stacking, in succession, N-length data vectors associated with each of the channels for each of the M pulses, signal presence is sought in one range cell at a time, the snapshots are censored so as to divide a set of K initial training data into (i) a set of K a covariance estimate is computed based on the uncensored snapshots, the covariance estimate is used in computing an adaptive coherence estimate values for the K threshold levels so computed are averaged over the uncensored range cells to yield a M-length threshold vector wherein each vector element corresponds to the quiescent estimate level for a particular Doppler, and the adaptive coherence estimate values for the K Preferably, the covariance matrix estimate comprised of only the K Preferably, the covariance matrix estimate, denoted {tilde over (R)} Preferably, the M-length threshold vector, denoted γ, is computed using the equation: Advantageously, the quiescent adaptive coherence estimate threshold level is scaled to obtain a desired level of false alarms. The threshold is preferably scaled using a minimum desired threshold level. Advantageously, scaled threshold, denoted τ According to a further aspect of the invention, there is provided a method for detecting a signal of a specific known form in the presence of clutter, said method comprising: dividing a set of initial training data derived from returns from a burst of identical pulses, into a set of censored data and a set of uncensored data; using the uncensored data to compute a covariance matrix estimate; using the covariance matrix estimate to compute adaptive coherence estimate values; computing an average adaptive coherence estimate threshold for each Doppler band so as to obtain a threshold; using the censored data of step (i) and the covariance matrix estimate of step (ii) to compute adaptive coherence estimate values for the uncensored data for each Doppler band; and comparing the adaptive coherence estimate values computed in step (v) with the respective thresholds computed in step (iv) to determine the presence or absence of the signal of a specific known form. Preferably, as discussed above, the covariance matrix estimate, denoted {tilde over (R)} As was also discussed above, preferably, the covariance matrix estimate, denoted {tilde over (R)} Preferably, the M-length threshold vector, denoted γ, is, computed using the equation: As discussed above, advantageously, the quiescent adaptive coherence estimate threshold level is scaled to obtain a desired level of false alarms. The threshold is preferably scaled using a minimum desired threshold level. Preferably, the scaled threshold, denoted τ In accordance with yet another aspect of the invention, there is provided a method for selecting a threshold for an adaptive coherence estimate detector and detecting a target signal using the threshold wherein a specific form of a target signal to be detected is known, said method comprising the steps of: receiving returns from a burst of M identical pulses transmitted over N radio frequency channels, sampling input data in the respective channels to form range cell samples for each pulse, forming snapshots by stacking, in succession, N-length data vectors associated with each of the channels for each of the M pulses, censoring the snapshots so as to divide a set of K initial training data into (i) a set of K computing a covariance estimate based on the uncensored snapshots, using the covariance estimate in computing an adaptive coherence estimate values for the K averaging the threshold levels so computed over the range cells corresponding to the uncensored snapshots to yield a M-length threshold vector wherein each vector element corresponds to the quiescent adaptive covariance estimate level for a particular Doppler; and comparing the adaptive coherence estimate values for the K Preferably, as discussed hereinabove, the covariance matrix estimate, denoted {tilde over (R)} Again, preferably, the covariance matrix estimate, denoted {tilde over (R)} Preferably, as discussed hereinbefore, the M-length threshold vector, γ, is computed using the equation: Again, the quiescent adaptive coherence estimate threshold level is preferably scaled to obtain a desired level of false alarms, the threshold is preferably scaled using a minimum desired threshold level, and most preferably, the scaled threshold, denoted τ Further features and advantages of the present invention will be set forth in, or apparent from, the detailed description of preferred embodiments thereof which follows. The single FIGURE in the drawings is a flow chart or block diagram of the basic steps employed in a detection method in accordance with a preferred embodiment of the invention. Considering some additional-background in connection with embodiments directed to radar applications, the Pulse Repetition Frequency (PRF) of the radar pulse scan determines the bandwidth of the Doppler frequency spectrum. The Doppler spectrum is partitioned into M frequency bands (for M consecutive pulses) in which targets are sought at each individual range gate of the radar platform by using the steering vector s The power level of the clutter return dictates to a large degree the threshold level for the ACE detector, according to the proximity to the clutter Doppler of a Doppler frequency band of interest. This can be illustrated by assuming the clutter return is comprised of a single Doppler frequency such that the measured data vector at a range cell that does contain a target can be modeled as z In order to provide a better understanding of the invention, consider a radar system that consists of an N-element antenna array which provides N Radio Frequency (RF) antenna channels. Time-delayed inputs of the N channels are to be combined via linear weighting to form an output such that an output performance measure (such as signal-to-noise (SNR) power ratio) is optimized. Assume that for each of these RF channels, the radar front end carries out amplification, filtering, reduction to baseband, and analog-to-digital (A/D) conversion. The output of each A/D is a data stream of in-phase and quadrature-phase (I, Q) output pairs. The I and Q components represent the real and imaginary parts, respectively, of the complex valued data stream. The radar waveform is assumed to be a burst of M identical pulses with pulse repetition interval (PRI) equal to T. Target detection is based upon the returns from this burst. The input data in the respective channels are sampled to form range-gate samples for each pulse. For the k In accordance with an important feature of preferred embodiments of the invention, the local snapshots (in terms of range) that may potentially contain a target are censored such that the set of K initial training data (ITD) snapshots are separated into a set of K As a consequence of splitting the data as described above, two covariance matrix estimates can be computed, viz., {tilde over (R)} To determine the level of the ACE threshold, a quiescent ACE level is computed which corresponds to the H More generally, equation (4), which employs the uncensored snapshots, is used to determine the level of the ACE threshold. Equation (3), which employs the censored snapshots, is used to derive the range value to be compared with the ACE threshold. It is noted that this approach is different from the way in which the CFAR test statistic is computed (see K. Gerlach and S. D. Blunt, “Efficient reiterative censoring of robust STAP using the FRACTA algorithm,” After the computations set forth above are completed, the local UTD ACEs for each Doppler are averaged over the uncensored range cells to yield the M-length threshold vector In order to obtain the desired level of false alarm, the ACE threshold for the m The foregoing may perhaps be better understood by referring to the single FIGURE of the drawings which is a flow chart or block diagram of a preferred embodiment of the invention, and is used to summarize the steps of the method described above. As shown, in a first, censoring step As indicated by step or block As shown by step or block In a parallel step or process indicated at Finally, as shown by step or block This overall method is referred to herein for shorthand purposes as the Doppler Sensitive-ACE (DS-ACE). An important advantage of DS-ACE thresholding is that targets at Doppler frequencies outside of the clutter spectrum are more easily detectable than with uniform thresholding of the ACE in Doppler. Furthermore, unlike uniform thresholding, DS-ACE is a straightforward automatic technique. The use of censored and uncensored training data sets produces more accurate covariance and resulting quiescent level estimations than does standard STAP which performs no censoring of the data. This has been verified using a validated high-fidelity clutter model wherein the automatic DS-ACE threshold method of the invention was found to detect nearly double the number of targets detected by uniform ACE thresholding that is set using trial-and-error techniques. Although the invention has been described above in relation to preferred embodiments thereof, it will be understood by those skilled in the art that variations and modifications can be effected in these preferred embodiments without departing from, the scope and spirit of the invention. Patent Citations
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